Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors

Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One...

Descripción completa

Detalles Bibliográficos
Autores principales: Faron, Anton, Opheys, Nikola S., Nowak, Sebastian, Sprinkart, Alois M., Isaak, Alexander, Theis, Maike, Mesropyan, Narine, Endler, Christoph, Sirokay, Judith, Pieper, Claus C., Kuetting, Daniel, Attenberger, Ulrike, Landsberg, Jennifer, Luetkens, Julian A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700660/
https://www.ncbi.nlm.nih.gov/pubmed/34943551
http://dx.doi.org/10.3390/diagnostics11122314
_version_ 1784620810875437056
author Faron, Anton
Opheys, Nikola S.
Nowak, Sebastian
Sprinkart, Alois M.
Isaak, Alexander
Theis, Maike
Mesropyan, Narine
Endler, Christoph
Sirokay, Judith
Pieper, Claus C.
Kuetting, Daniel
Attenberger, Ulrike
Landsberg, Jennifer
Luetkens, Julian A.
author_facet Faron, Anton
Opheys, Nikola S.
Nowak, Sebastian
Sprinkart, Alois M.
Isaak, Alexander
Theis, Maike
Mesropyan, Narine
Endler, Christoph
Sirokay, Judith
Pieper, Claus C.
Kuetting, Daniel
Attenberger, Ulrike
Landsberg, Jennifer
Luetkens, Julian A.
author_sort Faron, Anton
collection PubMed
description Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy.
format Online
Article
Text
id pubmed-8700660
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-87006602021-12-24 Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors Faron, Anton Opheys, Nikola S. Nowak, Sebastian Sprinkart, Alois M. Isaak, Alexander Theis, Maike Mesropyan, Narine Endler, Christoph Sirokay, Judith Pieper, Claus C. Kuetting, Daniel Attenberger, Ulrike Landsberg, Jennifer Luetkens, Julian A. Diagnostics (Basel) Article Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy. MDPI 2021-12-09 /pmc/articles/PMC8700660/ /pubmed/34943551 http://dx.doi.org/10.3390/diagnostics11122314 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Faron, Anton
Opheys, Nikola S.
Nowak, Sebastian
Sprinkart, Alois M.
Isaak, Alexander
Theis, Maike
Mesropyan, Narine
Endler, Christoph
Sirokay, Judith
Pieper, Claus C.
Kuetting, Daniel
Attenberger, Ulrike
Landsberg, Jennifer
Luetkens, Julian A.
Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
title Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
title_full Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
title_fullStr Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
title_full_unstemmed Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
title_short Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors
title_sort deep learning-based body composition analysis predicts outcome in melanoma patients treated with immune checkpoint inhibitors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700660/
https://www.ncbi.nlm.nih.gov/pubmed/34943551
http://dx.doi.org/10.3390/diagnostics11122314
work_keys_str_mv AT faronanton deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT opheysnikolas deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT nowaksebastian deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT sprinkartaloism deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT isaakalexander deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT theismaike deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT mesropyannarine deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT endlerchristoph deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT sirokayjudith deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT pieperclausc deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT kuettingdaniel deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT attenbergerulrike deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT landsbergjennifer deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors
AT luetkensjuliana deeplearningbasedbodycompositionanalysispredictsoutcomeinmelanomapatientstreatedwithimmunecheckpointinhibitors